Qwable-EX
A personal experimental linear merge of strong Qwen3.5-9B lineage models focused on structured reasoning, agentic coding, and tool-use behavior.
Qwable-EX combines the deliberate, trace-style reasoning of Empero’s Qwable fine-tune with complementary strengths from Qworus-V2 and Qwepus via a simple linear merge (Mergekit). It is intended as a research / experimentation checkpoint rather than a production model.
Model Details
| Property | Value |
|---|---|
| Base architecture | Qwen3.5-9B (dense, hybrid Gated DeltaNet + full attention) |
| Parameter count | ~9B |
| Merge method | Linear (Mergekit) |
| Primary lineage | Qwen3.5-9B → Qwable-style reasoning + complementary merges |
| Languages | Primarily English |
| License | MIT (this merge) — parent models are mostly Apache-2.0; see provenance notes |
Component Models
-
empero-ai/Qwable-9B-Claude-Fable-5
Full-parameter SFT of Qwen3.5-9B on Claude Fable 5 reasoning/coding traces + a small GPT-5.5 terminal/agent set. Strong structured<think>reasoning and agentic coding style. Vision tower was frozen (text-only fine-tune). -
DarkKitsune/Qwen3.5-9B-Qworus-V2
50/50 DARE-TIES merge ofempero-ai/Qwen3.8-9B-Distillandornith-ai/Ornith-1.5-9B. Hybrid reasoning model aimed at coding, tool calls, planning, and general question answering. Not uncensored by default. -
Netuoso/qwepus
Additional Qwen-based component included in the linear merge.
All components ultimately descend from the Qwen3.5-9B family (natively multimodal architecture with long context support).
Intended Uses
- Experimental local inference for structured reasoning and coding tasks
- Exploring how linear merges of Qwable-style + hybrid reasoning models behave
- Agentic / tool-use style prompting (results will vary)
- Research and personal experimentation
Not intended for production deployment, high-stakes decision making, or use cases requiring strong safety guarantees without additional filtering.
How to Use
This is a standard Transformers-compatible checkpoint. Example (text-only):
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "YOUR_USERNAME/Qwable-EX" # replace with actual repo
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "user", "content": "Write a clean Python function that merges two sorted lists."}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=2048,
do_sample=True,
temperature=0.7,
top_p=0.95,
repetition_penalty=1.05,
)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))